Estimating Technology Performance Improvement Rates by Mining Patent Data

73 Pages Posted: 16 Jun 2017 Last revised: 16 Jan 2018

See all articles by Giorgio Triulzi

Giorgio Triulzi

Massachusetts Institute of Technology (MIT); Universidad de los Andes, Colombia - School of Management

Jeff Alstott

Massachusetts Institute of Technology (MIT) - MIT Media Laboratory; Singapore University of Technology and Design (SUTD)

Christopher L. Magee

Massachusetts Institute of Technology

Date Written: January 15, 2018

Abstract

The future direction of technology development depends on the relative yearly rate of functional performance improvement of different technologies. We use patent data to identify accurate and reliable predictors of this rate for 30 technologies. We illustrate how patent-based predictors should be normalized to correct for possible confounding factors introduced by changing patenting dynamics. We test the accuracy and reliability of various predictors by means of a Monte Carlo cross-validation exercise. We find that a measure of the centrality of domains' patented inventions in the overall US patent citation network is an accurate and highly reliable predictor of improvement rates.

Keywords: Technology Performance, Exponential Rates, Patents, Citations, Centrality, Trajectories

JEL Classification: O32

Suggested Citation

Triulzi, Giorgio and Alstott, Jeff and Magee, Christopher L., Estimating Technology Performance Improvement Rates by Mining Patent Data (January 15, 2018). Available at SSRN: https://ssrn.com/abstract=2987588 or http://dx.doi.org/10.2139/ssrn.2987588

Giorgio Triulzi (Contact Author)

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Universidad de los Andes, Colombia - School of Management ( email )

Carrera Primera # 18A-12
Bogotá
Colombia

Jeff Alstott

Massachusetts Institute of Technology (MIT) - MIT Media Laboratory ( email )

20 Ames St.
Cambridge, MA 02139-4307
United States

Singapore University of Technology and Design (SUTD) ( email )

20 Dover Drive
Singapore, 138682
Singapore

Christopher L. Magee

Massachusetts Institute of Technology ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

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